Linearly constrained generalized Lloyd algorithm for reduced codebook vector quantization

As linearly constrained vector quantization (LCVQ) is efficient for block-based compression of images that require low complexity decompression, it is a “de facto” standard for three-dimensional (3-D) graphics cards that use texture compression. Motivated by the lack of an efficient algorithm for designing LCVQ codebooks, the generalized Lloyd (1982) algorithm (GLA) for vector quantizer (VQ) codebook improvement and codebook design is extended to a new linearly constrained generalized Lloyd algorithm (LCGLA). This LCGLA improves VQ codebooks that are formed as linear combinations of a reduced set of base codewords. As such, it may find application wherever linearly constrained nearest neighbor (NN) techniques are used, that is, in a wide variety of signal compression and pattern recognition applications that require or assume distributions that are locally linearly constrained. In addition, several examples of linearly constrained codebooks that possess desirable properties such as good sphere packing, low-complexity implementation, fine resolution, and guaranteed convergence are presented. Fast NN search algorithms are discussed. A suggested initialization procedure halves iterations to convergence when, to reduce encoding complexity, the encoder considers the improvement of only a single codebook for each block. Experimental results for image compression show that LCGLA iterations significantly improve the PSNR of standard high-quality lossy 6:1 LCVQ compressed images